Classification of PCG Signals Using A Nonlinear Autoregressive Network with Exogenous Inputs (NARX)

Khaled, Sara; Fakhry, Mahmoud; Mubarak, Ahmed S. A.

doi:10.1109/itce48509.2020.9047772

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…The NARX model integrates both the initial input data and the output data generated after training. In this way, the network can improve its capacity for continuous learning [22].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Araujo,

Bissiriou,

Villanueva

et al. 2024

Preprint

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Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through accurate diagnosis and classification of faults in three-phase induction motors, using artificial intelligence techniques, by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network, using the scanning method with multiple training and validation iterations, with the introduction of new data. The results of these tests showed that the network had excellent generalization in all the situations evaluated, achieving the following accuracy rates: Motor without fault = 94.2%, Unbalance fault = 95%, Bearings with fault =98% and Stator with fault = 95%.

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“…The NARX model integrates both the initial input data and the output data generated after training. In this way, the network can improve its capacity for continuous learning [22].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Araujo,

Bissiriou,

Villanueva

et al. 2024

Preprint

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“…In the study of [ 23 ], the authors used a pretrained CNN model (AlexNet) and achieved 87% recognition accuracy. The study in [ 24 ] aimed to use a nonlinear autoregressive network of exogenous inputs (NARX) for normal/abnormal classification of PCG signals from Physionet. In [ 25 ], the authors proposed a deep CNNs framework for heart acoustic classification using short segments of individual heartbeats.…”

Section: Related Workmentioning

confidence: 99%

Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application

2023

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The World Health Organization (WHO) highlights that cardiovascular diseases (CVDs) are one of the leading causes of death globally, with an estimated rise to over 23.6 million deaths by 2030. This alarming trend can be attributed to our unhealthy lifestyles and lack of attention towards early CVD diagnosis. Traditional cardiac auscultation, where a highly qualified cardiologist listens to the heart sounds, is a crucial diagnostic method, but not always feasible or affordable. Therefore, developing accessible and user-friendly CVD recognition solutions can encourage individuals to integrate regular heart screenings into their routine. Although many automatic CVD screening methods have been proposed, most of them rely on complex prepocessing steps and heart cycle segmentation processes. In this work, we introduce a simple and efficient approach for recognizing normal and abnormal PCG signals using Physionet data. We employ data selection techniques such as kernel density estimation (KDE) for signal duration extraction, signal-to-noise Ratio (SNR), and GMM clustering to improve the performance of 17 pretrained Keras CNN models. Our results indicate that using KDE to select the appropriate signal duration and fine-tuning the VGG19 model results in excellent classification performance with an overall accuracy of 0.97, sensitivity of 0.946, precision of 0.944, and specificity of 0.946.

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“…They offer to train visible and temporal properties of murmur in PCG signals through the use of short-time features of spectrogram and MFCCs. The nonlinear autoregressive network with exogenous inputs (NARX) is leveraged for binary the detection of heart abnormality in [35], [36]. This training and testing of this network is performed using three different groups of short-length features, namely, temporal, spectral, and statistical.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals

Fakhry

Brery

2022

IJECE

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<span lang="EN-US">Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification task can be tackled using the bidirectional long short-term memory (biLSTM) network, trained on features extracted from labeled PCG signals. Regarding the non-stationarity of PCG signals, it is recommended to extract the features from multiple short-length segments of the signals using a sliding window of certain shape and length. However, some window contains unfavorable spectral side lobes, which distort the features. Accordingly, it is preferable to adapt the window shape and length in terms of classification performance. We propose an experimental evaluation for three window shapes, each with three window lengths. The biLSTM network is trained and tested on statistical features extracted, and the performance is reported in terms of the window shapes and lengths. Results show that the best performance is obtained when the Gaussian window is used for splitting the signals, and the triangular window competes with the Gaussian window for a length of 75 ms. Although the rectangular window is a commonly offered option, it is the worst choice for splitting the signals. Moreover, the classification performance obtained with a 75 ms Gaussian window outperforms that of a baseline method.</span>

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Classification of PCG Signals Using A Nonlinear Autoregressive Network with Exogenous Inputs (NARX)

Cited by 10 publications

References 18 publications

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application

Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals

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